Neuromorphic and deep learning paradigms for neural data interpretation and computational neuroscience - Volume II

This Research Topic is part of the 'Neuromorphic and Deep Learning Paradigms for Neural Data Interpretation and Computational Neuroscience' series.

The integration of neuromorphic computing and deep learning continues to advance computational neuroscience, offering powerful approaches for interpreting large-scale, complex neural data. In Volume I of this Research Topic, contributions demonstrated how biologically inspired computation and data-driven learning can address challenges related to high dimensionality, temporal dynamics, and nonlinear neural processes. Building on these foundations, Volume II seeks to expand and refine these themes in light of recent methodological and technological progress.

As neuroscience research increasingly relies on large datasets from electrophysiology, neuroimaging, and brain–computer interfaces, both deep learning and neuromorphic systems play a growing role in decoding neural activity and modeling brain function. While deep learning approaches provide strong predictive performance, neuromorphic computing offers energy-efficient, event-driven alternatives that align more closely with biological principles. Continued integration of these paradigms is essential for improving scalability, interpretability, and real-world applicability.

This second volume encourages interdisciplinary contributions that extend prior work, explore emerging applications, and strengthen the link between neuroscience and artificial intelligence. By expanding on the themes introduced in Volume I, this Research Topic aims to further advance neural data interpretation and accelerate discoveries in computational neuroscience.

The scope of this Research Topic includes, but is not limited to:
- Neuromorphic algorithms and spiking neural networks for neural data analysis
- Deep learning approaches for decoding neural signals and brain states
- Biologically inspired AI models bridging neuroscience and machine learning
- Scalable methods for large-scale and multimodal neural data processing
- Computational models of brain function, learning, and cognition